Priority Ambulance - AI & Data Innovation Specialist

Position Summary

The AI & Data Innovation Specialist supports the Priority Ambulance Family of Companies by developing, implementing, and maintaining artificial intelligence and data analytics solutions that enhance operational efficiency, workforce management, clinical outcomes, and organizational decision-making.

This role serves as a bridge between data science and EMS operations, leveraging technology to advance the company’s S.A.F.E. values (Safe, Accountable, Friendly, Efficient) and supporting the organization’s Healthcare Anywhere mission through innovation, automation, and actionable insights.

Essential Duties and Responsibilities

• Design, develop, and deploy AI and predictive models to forecast EMS demand, optimize unit deployment, and enhance workforce scheduling.
• Analyze large-scale datasets from CAD, ePCR, HRIS, payroll, fleet maintenance, and billing systems to identify trends, inefficiencies, and opportunities for improvement.
• Collaborate with cross-functional teams (Operations, HR, Finance, IT, Clinical) to define analytics requirements and develop data-driven solutions.
• Implement machine learning algorithms to support clinical quality assurance, documentation review, and real-time performance dashboards.
• Create and maintain data visualization dashboards (Power BI, Tableau) to communicate operational KPIs to leadership in clear, actionable formats.
• Partner with IT and Compliance teams to ensure HIPAA-compliant data handling, model transparency, and ethical AI practices.
• Support automation of manual processes across administrative, HR, and operations functions using AI tools or low-code automation platforms.
• Contribute to the development of the company’s Innovation Enablement strategy, ensuring alignment between technology solutions and enterprise goals.
• Provide technical leadership on data science projects, including data cleaning, model training, and API integration with enterprise platforms.
• Train leaders and staff on interpreting and using AI-driven insights in daily decision-making.

Knowledge, Skills, and Abilities

• Advanced knowledge of machine learning techniques, predictive analytics, and statistical modeling.
• Strong proficiency in Python, R, SQL, and related AI/ML frameworks (TensorFlow, scikit-learn, PyTorch).
• Familiarity with Power BI, Tableau, or Looker for analytics visualization.
• Experience with cloud data systems (Azure, AWS, or Google Cloud).
• Understanding of EMS or healthcare data environments, including CAD, NEMSIS/ePCR, and HRIS data.
• Excellent communication skills—able to translate complex analytics into executive-ready insights.
• Proven ability to work collaboratively across operational, technical, and clinical teams.
• Demonstrated ethical judgment in data privacy, bias management, and AI transparency.

Work Environment

This position may operate in a hybrid or remote setting, with occasional travel to company operations or regional offices for project collaboration and training.

Education and Experience

• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field required.
• Master’s degree or advanced certification in Artificial Intelligence or Machine Learning preferred.
• Minimum of 3–5 years of professional experience in AI, data science, or predictive analytics.
• Prior experience in EMS, healthcare, or public safety environments preferred.
• Familiarity with ESO, Zoll, ImageTrend, or FirstWatch systems a plus.

Preferred Certifications

• Google Cloud Certified – Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure Data Scientist Associate
• Certified Health Data Analyst (CHDA) or equivalent

Core Competencies

• Innovation Enablement: Drives adoption of emerging technologies and AI-based tools across the organization.
• Operational Excellence: Leverages data to improve efficiency, reliability, and resource utilization.
• Collaboration: Partners effectively with leadership and frontline teams to deliver impactful results.
• Strategic Mindset: Aligns AI initiatives with organizational growth, scalability, and quality improvement goals.
• Integrity & Compliance: Upholds ethical standards, patient privacy, and data governance principles.

Performance Indicators

• Reduction in average response times through predictive deployment modeling.
• Decrease in QA review time via automated data validation.
• Improvement in forecasting accuracy for call volume and staffing.
• Cost savings achieved through process automation and efficiency gains.
• Leadership adoption rate of AI dashboards and tools.